SANTA CLARA, CA -- (Marketwire) -- 07/05/2011 -- NVIDIA today named Stanford University as a CUDA Center of Excellence, honoring the institution's pioneering work in parallel computing research using NVIDIA® CUDA® technology and NVIDIA GPUs.
The Institute for Computational & Mathematical Engineering (ICME) at the Stanford School of Engineering will spearhead the university's CUDA Center of Excellence program in partnership with a number of other departments, including the Department of Computer Science (CS), the Center for Computational Earth and Environmental Sciences (CEES) and the Department of Mechanical Engineering, Flow Physics Division.
The CUDA Center of Excellence program recognizes, rewards and fosters collaboration with leading institutions at the forefront of parallel computing research. A world leader in computational mathematics, scientific computing and computer science, Stanford joins a network of 11 elite institutions worldwide that have demonstrated a unique vision for improving the technology and application of parallel computing, and are empowering academics and scientists to conduct world-changing research.
Stanford currently offers a number of full courses, short courses and partner-sponsored courses covering CUDA architecture and parallel computing. As a CUDA Center of Excellence, Stanford will utilize GPU computing equipment and grants provided by NVIDIA to support a number of research and academic programs, including:
"It's vitally important that our faculty be at the forefront of computing technology so that we can continue developing state-of-the-art computational algorithms that drive innovation in the sciences and engineering," said Margot Gerritsen, director, Institute for Computational & Mathematical Engineering, and associate professor, Department of Energy Resources Engineering, at Stanford University. "This award allows us to broadly expand parallel computing education and research programs to large numbers of researchers and students from a wide variety of disciplines."
The CUDA Center of Excellence program is competitive and prestigious, and any institution with a demonstrated commitment to both parallel computing research and education may apply for CCOE status.
Other CUDA Centers of Excellence include: Georgia Tech, Harvard University, Institute of Process Engineering at the Chinese Academy of Sciences, National Taiwan University, Tokyo Tech, Tsinghua University (China), University of Cambridge, University of Illinois at Urbana-Champaign, University of Maryland, University of Tennessee, and University of Utah. For more information on the NVIDIA CUDA Center of Excellence program, visit: http://research.nvidia.com/content/cuda-centers-excellence.
CUDA is NVIDIA's parallel computing architecture, which enables dramatic increases in computing performance by harnessing the power of GPUs. NVIDIA CUDA GPUs support all GPU computing programming models, APIs, and languages, including CUDA C/C++/Fortran, OpenCL, DirectCompute, and the recently announced Microsoft C++ AMP.
More than 450 universities and institutions worldwide teach the CUDA programming model within their curriculum. For more information on NVIDIA CUDA technology, visit: www.nvidia.com/CUDA.
About Stanford University and the ICME
Stanford University is internationally renowned for its programs in computational mathematics, scientific computing and computer science. The Institute for Computational & Mathematical Engineering (ICME) at Stanford University is focused on training undergraduate and graduate students and scholars in mathematical modeling, scientific computing and advanced computational algorithms. The institute has made significant contributions in a variety of areas, including: fluid and solid mechanics, computer graphics, reservoir modeling, bio-engineering, uncertainty quantification, stochastics, optimization, and financial mathematics. ICME offers both MS and Ph.D. degrees, and its 140 graduate students are advised by 45 associated faculty, who represent 16 different departments in four schools on campus.
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NVIDIA, CUDA, GPU, GPU computing, supercomputing, parallel computing, GPGPU, high performance computing, Stanford University, ICME, OpenCL, DirectCompute, developers, research, scientific computing
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